Digital Design Pathway (with Awareness)

CCAT’s Digital Design Week immerses participants in the fundamentals of Model-Based Definition (MBD) and model-centric design practices that drive the digital thread. Engineers, designers, and manufacturing professionals will learn how to create, apply, and validate Product Manufacturing Information (PMI) within 3D models, ensuring they serve as the single source of truth across the enterprise.

Description

 

CCAT's Digital Design training pathway includes the following courses over four days:

  • Tuesday - Awareness:
    • Digital Thread Concepts and Value Proposition
    • Next Gen TDPs and MBD Workflows
  • Wednesday:
    • Introduction to CAD using MBD
    • Intermediate CAD using MBD
  • Thursday:
    • CAD Quality and Validation Practices
    • Interpreting CAD Quality and Validation
  • Friday:
    • Digital Costing for MBD
    • Product Lifecycle Management for MBD

Target Audience

The Digital Design Week training is ideal for design engineers, CAD specialists, product developers, and manufacturing professionals who want to enhance their skills in Model-Based Definition (MBD) and model-centric design practices. It is designed for those responsible for creating, managing, and sharing 3D product data across the organization and supply chain.

Learning Objectives

  • Understand the fundamentals of Model-Based Definition (MBD) and its role in enabling the digital thread.
  • Create and manage Product Manufacturing Information (PMI) within 3D CAD models to establish a single source of truth for design intent.
  • Validate MBD-driven models to ensure accuracy, compliance, and readiness for downstream consumption across design, quality, and production.
  • Leverage digital design and manufacturing data to develop accurate, model-driven cost analyses throughout the product lifecycle.
  • Collaborate more effectively with cross-functional teams by delivering standardized, consistent, and interoperable 3D data packages.
  • Recognize the impact of MBD adoption on reducing errors, improving efficiency, and accelerating time-to-production within the digital manufacturing ecosystem.

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